Abstract: [Objective] This paper was to establish a model of remote sensing information, and the forest site class index was successfully estimated. The spatial distribution of forest site quality was analyzed systematically and scientifically, which provides certain data support and theoretical basis for forest ecosystem management and afforestation.[Method] In this study, Daxing'an Mountain in Heilongjiang Province was taken as the research area, two types of response variables, including the remote sensing factors (modified soil vegetation index,MSVI;difference vegetation index,DVI) and the stand factors (average diameter at breast height,ADBH; forest canopy closure,FCC) were considered in the modeling processes. Both global and GWR (geographically weighted regression) modeling techniques were utilized to fit the models to evaluate and analyze the site quality of the study area and to explore the spatial distribution of forest site class index along with the changing topography. By comparing the two method, we finally chose the GWR model to map the site class index space distribution. The global Moran I index was used to characterize the spatial autocorrelation of the model residuals at different spatial scales (8 km to 80 km).[Result] The result showed that the spatial distribution of the site class index in Daxing'an Mountain region tended to be a clustered distribution, and a high site quality index appeared in the northeastern part of the study area while the southwestern portion with a low site quality index, also the maximum value was observed in the northern region. Both remote sensing factors and stand factors affect the distribution of forest site class index.The GWR model outperformed the global model in both model fitting and validation performances. The R_adj² of the globe model was 0.48, the AIC was 1 816 with a RMSE of 1.74, while the R_adj² of the GWR model was 0.53, the AIC was 1 784 and the RMSE was 1.29.[Conclusion] Global model and GWR model can effectively estimate forest site class index, the GWR model can solve the spatial autocorrelation of the model residuals, and generate more ideal prediction result, which is feasible to estimate the site class index.

Key words: site quality, multi-spectral remote sensing, geographically weighted regression (GWR) model, multiple linear regression model